As dependence of the bulk electric power system on gas-fired generation grows, more economically efficient coordination between the wholesale natural gas and electricity markets is increasingly important. New tools are needed to achieve more efficient and reliable operation of both markets by providing participants more accurate price signals on which to base their investment and operating decisions.

Today’s Electricity energy prices are consistent with the physical flow of electric energy in the power grid because of the economic optimization of power system operation in organized electricity markets administered by Regional Transmission Organizations (RTOs). A similar optimization approach that accounts for physical and engineering factors of pipeline hydraulics and compressor station operations would lead to location- and time-dependent intra-day prices of natural gas consistent with pipeline engineering factors, operations, and the physics of gas flow.

More economically efficient gas-electric coordination is envisioned as the timely exchange of both physical and pricing data between participants in each market, with price formation in both markets being fully consistent with the physics of energy flow. Physical data would be intra-day (e.g., hourly) gas schedules (burn and delivery) and pricing data would be bids and offers reflecting willingness to pay and to accept. Here, we describe the economic concepts related to this exchange, and discuss the regulatory and institutional issues that must be addressed. We then formulate an intra-day pipeline market clearing problem whose solution provides a flow schedule and hourly pricing, while ensuring that pipeline hydraulic limitations, compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. Furthermore, in order to support the practical application of these concepts, we provide a computational example of gas pipeline market clearing on a small interpretable model, and validate the results using a commercial pipeline simulator. Finally, we validate the modeling by cross-verifying simulations with SCADA data measured on a real pipeline system.